class_docstr: Apply additive zero-centered Gaussian noise.
  
  This is useful to mitigate overfitting
  (you could see it as a form of random data augmentation).
  Gaussian Noise (GS) is a natural choice as corruption process
  for real valued inputs.
  
  As it is a regularization layer, it is only active at training time.
  
  Args:
      stddev: Float, standard deviation of the noise distribution.
      seed: Integer, optional random seed to enable deterministic behavior.
  
  Call arguments:
      inputs: Input tensor (of any rank).
      training: Python boolean indicating whether the layer should behave in
          training mode (adding noise) or in inference mode (doing nothing).
code: def call(self, inputs, training=False):
      if training and self.stddev > 0:
          return inputs + backend.random.normal(
              shape=ops.shape(inputs),
              mean=0.0,
              stddev=self.stddev,
              seed=self.seed_generator,
          )
      return inputs
  
file_path: keras/keras/layers/regularization/gaussian_noise.py
imports: from keras import backend
  from keras import layers
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 2 for calling the GaussianNoise obj's underlying member method code with example usage: obj.call(inputs, training) and returns: inputs, unknown